Use the exponential smoothing model that you selected in part a to forecast these data. Use no holdout period and requests 12 months of future forecasts in StatTools. Use the default smoothing constant of 0.1. Attach the StatTools report. Also, include a plot of the forecast and actual sales over time. 3) Repeat part 2, optimizing the smoothing constant. Does the optimization parameter option make much of an improvement? Attach the StatTools report. Also, include a plot of the forecast and actual sales over time.
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The file Retail Sales ( please show the models ) contains monthly revenue for a company over a four-year period. Our goal is to forecast this company’s revenue for the 12 months.
1)Create and attach the time series plot of the monthly sales. Does this time series exhibit trend and/or seasonality? Based on what you see in the chart, which exponential smoothing models do you think should be used for forecasting? Why?
2) Use the exponential smoothing model that you selected in part a to forecast these data. Use no holdout period and requests 12 months of future forecasts in StatTools. Use the default smoothing constant of 0.1. Attach the StatTools report. Also, include a plot of the forecast and actual sales over time.
3) Repeat part 2, optimizing the smoothing constant. Does the optimization parameter option make much of an improvement? Attach the StatTools report. Also, include a plot of the forecast and actual sales over time.
4)Repeat part d, tunning the smoothing parameter(s). Attach the StatTools report. Also, include a plot of the forecast and actual sales over time.
5)Use the moving average method to forecast this company’s revenue for the 12 months. Use no holdout period and requests 12 months of future forecasts in StatTools. What span seems to work the best? Attach the StatTools report. Also, include a plot of the forecast and actual sales over time.
6) Write a short summary of your results. Would you say the forecasts are adequate?
